{"cells":[{"cell_type":"markdown","id":"470fb281","metadata":{},"source":["### Regarding Warnings\n","* Warning in the notebook is due to the discrepancy in package version between local and server environment\n","* Readers can ignore them or update the packages to the required version through \"pip install [package]==[version]\""]},{"cell_type":"markdown","id":"24ef7c6b","metadata":{},"source":["### Session Creation"]},{"cell_type":"code","execution_count":27,"id":"c81ff43a-242e-499c-8de2-62a0971bc2b9","metadata":{"executionInfo":{"elapsed":4954,"status":"ok","timestamp":1701595097461,"user":{"displayName":"Vivek Ss","userId":"14764910798655109096"},"user_tz":-330},"id":"c81ff43a-242e-499c-8de2-62a0971bc2b9"},"outputs":[],"source":["from snowflake.snowpark import Session\n","from snowflake.snowpark.functions import col\n","import configparser\n","\n","\n","# Loading Credentials From Config File\n","snowflake_credentials_file = '../snowflake_creds.config'\n","config = configparser.ConfigParser()\n","config.read(snowflake_credentials_file)\n","connection_parameters = dict(config['default'])\n","session = Session.builder.configs(connection_parameters).create()"]},{"cell_type":"markdown","id":"b83d369e","metadata":{},"source":["##### Reading BSD_TRAINING Table\n","* BSD_TRAINING table is loaded in chapter 5\n","* Refer chapter_5.ipynb to create BSD_TRAINING table"]},{"cell_type":"code","execution_count":28,"id":"83a288d3-2b5b-4c73-8523-79d5e9a1d04f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2577,"status":"ok","timestamp":1701595226243,"user":{"displayName":"Vivek Ss","userId":"14764910798655109096"},"user_tz":-330},"id":"83a288d3-2b5b-4c73-8523-79d5e9a1d04f","outputId":"14f877c9-c6b4-4658-e6c4-8be63e94b542"},"outputs":[{"name":"stdout","output_type":"stream","text":["-------------------------------------------------------------------------------------------------------------------------------------\n","|\"SEASON\"  |\"HOLIDAY\"  |\"WORKINGDAY\"  |\"WEATHER\"  |\"TEMP\"  |\"ATEMP\"  |\"HUMIDITY\"  |\"WINDSPEED\"  |\"CASUAL\"  |\"REGISTERED\"  |\"COUNT\"  |\n","-------------------------------------------------------------------------------------------------------------------------------------\n","|1         |0          |0             |1          |9.84    |14.395   |81          |0.0          |3         |13            |16       |\n","|1         |0          |0             |1          |9.02    |13.635   |80          |0.0          |8         |32            |40       |\n","-------------------------------------------------------------------------------------------------------------------------------------\n","\n"]}],"source":["FEATURE_LIST = [ \"HOLIDAY\", \"WORKINGDAY\", \"HUMIDITY\", \"TEMP\", \"ATEMP\", \"WINDSPEED\", \"SEASON\", \"WEATHER\"]\n","LABEL_COLUMNS = ['COUNT']\n","OUTPUT_COLUMNS = ['PREDICTED_COUNT']\n","\n","df = session.table(\"BSD_TRAINING\")\n","df = df.drop(\"DATETIME\",\"DATE\")\n","df.show(2)"]},{"cell_type":"markdown","id":"cbe5fd90","metadata":{},"source":["### Preparing the Model -  XGBoost\n","\n","* Code block delineates building XGboost model by optimizing two parameters through Gridsearch\n","* Contains code snippets to find optimal model parameters - max_depth & min_child_weight"]},{"cell_type":"code","execution_count":3,"id":"080328b2-d7a2-4dee-8e63-3d8035a69126","metadata":{"id":"080328b2-d7a2-4dee-8e63-3d8035a69126"},"outputs":[],"source":["from snowflake.ml.modeling.model_selection import GridSearchCV\n","from snowflake.ml.modeling.xgboost import XGBRegressor\n","\n","\n","param_grid = {\n","        \"max_depth\":[3, 4, 5, 6, 7, 8],\n","        \"min_child_weight\":[1, 2, 3, 4],\n","}\n","\n","grid_search = GridSearchCV(\n","    estimator=XGBRegressor(),\n","    param_grid=param_grid,\n","    n_jobs = -1,\n","    scoring=\"neg_root_mean_squared_error\",\n","    input_cols=FEATURE_LIST,\n","    label_cols=LABEL_COLUMNS,\n","    output_cols=OUTPUT_COLUMNS\n",")"]},{"cell_type":"code","execution_count":4,"id":"3cbf4466-02f2-4c1c-a58e-bf89921f9828","metadata":{"id":"3cbf4466-02f2-4c1c-a58e-bf89921f9828","outputId":"8011f549-c51a-4c9c-8abe-f56ea3b43266"},"outputs":[{"name":"stderr","output_type":"stream","text":["The version of package 'xgboost' in the local environment is 1.7.6, which does not fit the criteria for the requirement 'xgboost==1.7.3'. Your UDF might not work when the package version is different between the server and your local environment.\n"]},{"name":"stdout","output_type":"stream","text":["[19:09:25] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0fdc6d574b9c0d168-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:553: \n","  If you are loading a serialized model (like pickle in Python, RDS in R) generated by\n","  older XGBoost, please export the model by calling `Booster.save_model` from that version\n","  first, then load it back in current version. See:\n","\n","    https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html\n","\n","  for more details about differences between saving model and serializing.\n","\n"]},{"data":{"text/plain":["<snowflake.ml.modeling.model_selection.grid_search_cv.GridSearchCV at 0x1fc55a09370>"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["train_df, test_df = df.random_split(weights=[0.7, 0.3], seed=0) \n","grid_search.fit(train_df)"]},{"cell_type":"code","execution_count":5,"id":"9399a324-d5e8-45a3-80bb-28a460ab0c52","metadata":{"id":"9399a324-d5e8-45a3-80bb-28a460ab0c52","outputId":"d762a732-29b3-4ed1-c47a-4dbe8ed6ddb6"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n","             colsample_bylevel=None, colsample_bynode=None,\n","             colsample_bytree=None, early_stopping_rounds=None,\n","             enable_categorical=False, eval_metric=None, feature_types=None,\n","             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","             interaction_constraints=None, learning_rate=None, max_bin=None,\n","             max_cat_threshold=None, max_cat_to_onehot=None,\n","             max_delta_step=None, max_depth=3, max_leaves=None,\n","             min_child_weight=2, missing=nan, monotone_constraints=None,\n","             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n","             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n","             colsample_bylevel=None, colsample_bynode=None,\n","             colsample_bytree=None, early_stopping_rounds=None,\n","             enable_categorical=False, eval_metric=None, feature_types=None,\n","             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","             interaction_constraints=None, learning_rate=None, max_bin=None,\n","             max_cat_threshold=None, max_cat_to_onehot=None,\n","             max_delta_step=None, max_depth=3, max_leaves=None,\n","             min_child_weight=2, missing=nan, monotone_constraints=None,\n","             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n","             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"],"text/plain":["XGBRegressor(base_score=None, booster=None, callbacks=None,\n","             colsample_bylevel=None, colsample_bynode=None,\n","             colsample_bytree=None, early_stopping_rounds=None,\n","             enable_categorical=False, eval_metric=None, feature_types=None,\n","             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","             interaction_constraints=None, learning_rate=None, max_bin=None,\n","             max_cat_threshold=None, max_cat_to_onehot=None,\n","             max_delta_step=None, max_depth=3, max_leaves=None,\n","             min_child_weight=2, missing=nan, monotone_constraints=None,\n","             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n","             predictor=None, random_state=None, ...)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["grid_search.to_sklearn().best_estimator_"]},{"cell_type":"code","execution_count":6,"id":"a77e3ac1-c818-4c5f-b525-b32e88515727","metadata":{"id":"a77e3ac1-c818-4c5f-b525-b32e88515727","outputId":"9109f7dd-fb98-4d62-a6b5-67c0785e78ab"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\Admin\\anaconda3\\envs\\def_gui_3.8_env\\lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n","  self._figure.tight_layout(*args, **kwargs)\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 588.986x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","gs_results = grid_search.to_sklearn().cv_results_\n","max_depth_val = []\n","min_child_weight_val = []\n","for param_dict in gs_results[\"params\"]:\n","    max_depth_val.append(param_dict[\"max_depth\"])\n","    min_child_weight_val.append(param_dict[\"min_child_weight\"])\n","mape_val = gs_results[\"mean_test_score\"]*-1\n","\n","gs_results_df = pd.DataFrame(data={\n","    \"max_depth\":max_depth_val,\n","    \"min_child_weight\":min_child_weight_val,\n","    \"mape\":mape_val})\n","\n","sns.relplot(data=gs_results_df, x=\"min_child_weight\", y=\"mape\", hue=\"max_depth\", kind=\"line\")\n","\n","plt.show()"]},{"cell_type":"code","execution_count":7,"id":"bf2d1522-6bff-494c-9b19-92c3e08e5f00","metadata":{"id":"bf2d1522-6bff-494c-9b19-92c3e08e5f00","outputId":"450fd1c7-e6f8-424f-e013-505d5746cbaa"},"outputs":[{"name":"stderr","output_type":"stream","text":["The version of package 'xgboost' in the local environment is 1.7.6, which does not fit the criteria for the requirement 'xgboost==1.7.3'. Your UDF might not work when the package version is different between the server and your local environment.\n"]},{"name":"stdout","output_type":"stream","text":["-------------------------------\n","|\"COUNT\"  |\"PREDICTED_COUNT\"  |\n","-------------------------------\n","|1        |31                 |\n","|3        |25                 |\n","|36       |102                |\n","|94       |145                |\n","|106      |146                |\n","|93       |137                |\n","|37       |75                 |\n","|28       |86                 |\n","|39       |111                |\n","|17       |85                 |\n","-------------------------------\n","\n","Mean absolute percentage error: 4.529012115642\n"]}],"source":["from snowflake.ml.modeling.metrics import mean_absolute_percentage_error\n","\n","result = grid_search.predict(test_df)\n","mape = mean_absolute_percentage_error(df=result,\n","                                        y_true_col_names=\"COUNT\",\n","                                        y_pred_col_names=\"PREDICTED_COUNT\")\n","\n","result.select(\"COUNT\", \"PREDICTED_COUNT\").show()\n","print(f\"Mean absolute percentage error: {mape}\")"]},{"cell_type":"code","execution_count":8,"id":"f3237782-2082-404a-b6e1-ebc935bb9823","metadata":{"id":"f3237782-2082-404a-b6e1-ebc935bb9823","outputId":"8f33a9d2-11c4-49bf-9b43-c55a9aac81f5"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\Admin\\anaconda3\\envs\\def_gui_3.8_env\\lib\\site-packages\\seaborn\\axisgrid.py:123: UserWarning: The figure layout has changed to tight\n","  self._figure.tight_layout(*args, **kwargs)\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["g = sns.relplot(data=result[\"COUNT\", \"PREDICTED_COUNT\"].to_pandas().astype(\"float64\"), x=\"COUNT\", y=\"PREDICTED_COUNT\", kind=\"scatter\")\n","g.ax.axline((0,0), slope=1, color=\"r\")\n","\n","plt.show()"]},{"cell_type":"markdown","id":"0e2f4dfb","metadata":{},"source":["### Registering The Model\n"]},{"cell_type":"code","execution_count":9,"id":"dace15c6-47a1-4c18-bcd8-514966dde657","metadata":{"id":"dace15c6-47a1-4c18-bcd8-514966dde657"},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_21264\\1983920624.py:2: DeprecationWarning: \n","The `snowflake.ml.registry.model_registry.ModelRegistry` has been deprecated starting from version 1.2.0.\n","It will stay in the Private Preview phase. For future implementations, kindly utilize `snowflake.ml.registry.Registry`,\n","except when specifically required. The old model registry will be removed once all its primary functionalities are\n","fully integrated into the new registry.\n","        \n","  registry = model_registry.ModelRegistry(session=session, database_name=\"SNOWPARK_DEFINITVE_GUIDE\", schema_name=\"My_SCHEMA\", create_if_not_exists=True)\n","create_model_registry() is in private preview since 0.2.0. Do not use it in production. \n","WARNING:absl:The database SNOWPARK_DEFINITVE_GUIDE already exists. Skipping creation.\n","WARNING:absl:The schema SNOWPARK_DEFINITVE_GUIDE.\"My_SCHEMA\" already exists. Skipping creation.\n"]}],"source":["from snowflake.ml.registry import model_registry\n","registry = model_registry.ModelRegistry(session=session, database_name=\"SNOWPARK_DEFINITVE_GUIDE\", schema_name=\"My_SCHEMA\", create_if_not_exists=True)\n","\n","optimal_model = grid_search.to_sklearn().best_estimator_\n","optimal_max_depth = grid_search.to_sklearn().best_estimator_.max_depth\n","optimal_min_child_weight = grid_search.to_sklearn().best_estimator_.min_child_weight\n","\n","optimal_mape = gs_results_df.loc[(gs_results_df['max_depth']==optimal_max_depth) &\n","                                 (gs_results_df['min_child_weight']==optimal_min_child_weight), 'mape'].values[0]"]},{"cell_type":"code","execution_count":10,"id":"9cb244fa","metadata":{},"outputs":[],"source":["from snowflake.ml._internal.utils import identifier \n","\n","\n","db = identifier._get_unescaped_name(session.get_current_database()) \n","schema = identifier._get_unescaped_name(session.get_current_schema()) "]},{"cell_type":"code","execution_count":16,"id":"ac7d1d3e-ef2b-43da-9047-e521b379d578","metadata":{"id":"ac7d1d3e-ef2b-43da-9047-e521b379d578","outputId":"e4c0dedc-8eeb-4fbd-e3a3-480f600d7d25"},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Users\\Admin\\anaconda3\\envs\\def_gui_3.8_env\\lib\\site-packages\\snowflake\\ml\\model\\_packager\\model_env\\model_env.py:127: UserWarning: Package requirement anyio<4,>=3.5.0 specified, while version 4.2.0 is installed. Local version will be ignored to conform to package requirement.\n","  req_to_add = env_utils.get_local_installed_version_of_pip_package(req_to_check)\n","c:\\Users\\Admin\\anaconda3\\envs\\def_gui_3.8_env\\lib\\site-packages\\snowflake\\ml\\model\\model_signature.py:69: UserWarning: The sample input has 100 rows, thus a truncation happened before inferring signature. This might cause inaccurate signature inference. If that happens, consider specifying signature manually.\n","  warnings.warn(\n"]}],"source":["model_name = \"bike_model_xg_boost\"\n","model_version = 1\n","X = train_df.select(FEATURE_LIST).limit(100) \n","registry.log_model(\n","    model_name=model_name,\n","    model_version=model_version,\n","    model=optimal_model,\n","    sample_input_data=X,\n","    options={\"embed_local_ml_library\": True, \"relax\": True})\n","\n","registry.set_metric(model_name=model_name, model_version=model_version, metric_name=\"mean_abs_pct_err\", metric_value=optimal_mape)"]},{"cell_type":"code","execution_count":15,"id":"645b7aae","metadata":{},"outputs":[],"source":["registry.delete_model(\"bike_model_xg_boost\",1)"]},{"cell_type":"code","execution_count":null,"id":"1dbff7d8-3f00-4e86-915d-6632a0a445cb","metadata":{"id":"1dbff7d8-3f00-4e86-915d-6632a0a445cb","outputId":"34da2259-550b-4e4b-fcd4-cc9412ce0d06"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CREATION_CONTEXT</th>\n","      <th>CREATION_ENVIRONMENT_SPEC</th>\n","      <th>CREATION_ROLE</th>\n","      <th>CREATION_TIME</th>\n","      <th>ID</th>\n","      <th>INPUT_SPEC</th>\n","      <th>NAME</th>\n","      <th>OUTPUT_SPEC</th>\n","      <th>RUNTIME_ENVIRONMENT_SPEC</th>\n","      <th>TYPE</th>\n","      <th>URI</th>\n","      <th>VERSION</th>\n","      <th>ARTIFACT_IDS</th>\n","      <th>DESCRIPTION</th>\n","      <th>METRICS</th>\n","      <th>TAGS</th>\n","      <th>REGISTRATION_TIMESTAMP</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>None</td>\n","      <td>{\\n  \"python\": \"3.8.16\"\\n}</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>2024-04-16 06:26:17.512000-07:00</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>None</td>\n","      <td>bike_model_xg_boost</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>xgboost</td>\n","      <td>sfc://SNOWPARK_DEFINITVE_GUIDE.\"My_SCHEMA\".SNO...</td>\n","      <td>1</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>{\\n  \"mean_abs_pct_err\": 164.38951873779297\\n}</td>\n","      <td>None</td>\n","      <td>2024-04-16 06:26:18.541000-07:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  CREATION_CONTEXT   CREATION_ENVIRONMENT_SPEC   CREATION_ROLE  \\\n","0             None  {\\n  \"python\": \"3.8.16\"\\n}  \"ACCOUNTADMIN\"   \n","\n","                     CREATION_TIME                                ID  \\\n","0 2024-04-16 06:26:17.512000-07:00  e00fa52bfbf411ee89ba34f39a51dc3f   \n","\n","  INPUT_SPEC                 NAME OUTPUT_SPEC RUNTIME_ENVIRONMENT_SPEC  \\\n","0       None  bike_model_xg_boost        None                     None   \n","\n","      TYPE                                                URI VERSION  \\\n","0  xgboost  sfc://SNOWPARK_DEFINITVE_GUIDE.\"My_SCHEMA\".SNO...       1   \n","\n","  ARTIFACT_IDS DESCRIPTION                                         METRICS  \\\n","0         None        None  {\\n  \"mean_abs_pct_err\": 164.38951873779297\\n}   \n","\n","   TAGS           REGISTRATION_TIMESTAMP  \n","0  None 2024-04-16 06:26:18.541000-07:00  "]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["#LIST MODELS IN THE REGISTRY\n","\n","registry.list_models().to_pandas()"]},{"cell_type":"markdown","id":"5148f072","metadata":{},"source":["### Model Registry Method\n","* Code snippets covers all vital model registry methods. Methods include as following\n","* Set, get & remove tags / Set model description\n","* Ways to access registry history & finding evaluation metric\n","* Loading model from registry"]},{"cell_type":"code","execution_count":17,"id":"c8f8cb40-068d-492c-bc93-9ec7d05c0b1a","metadata":{"id":"c8f8cb40-068d-492c-bc93-9ec7d05c0b1a","outputId":"d6f0e9c9-38f9-4d6a-a3bd-b47e272631bc"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.set_tag() is in private preview since 0.2.0. Do not use it in production. \n","WARNING:snowflake.snowpark:ModelRegistry.get_tags() is in private preview since 0.2.0. Do not use it in production. \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>NAME</th>\n","      <th>TAGS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>bike_model_xg_boost</td>\n","      <td>{\\n  \"usage\": \"experiment\"\\n}</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  NAME                           TAGS\n","0  bike_model_xg_boost  {\\n  \"usage\": \"experiment\"\\n}"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["#set tag for your model\n","\n","registry.set_tag(model_name=model_name, model_version=model_version, tag_name=\"usage\", tag_value=\"experiment\")\n","registry.list_models().to_pandas()[[\"NAME\", \"TAGS\"]]"]},{"cell_type":"code","execution_count":18,"id":"85fec4f4-10bd-4b51-bded-2d9bb9bba87b","metadata":{"id":"85fec4f4-10bd-4b51-bded-2d9bb9bba87b","outputId":"a25de16b-b02a-4460-e2dc-99acfbef5a92"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.remove_tag() is in private preview since 0.2.0. Do not use it in production. \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>NAME</th>\n","      <th>TAGS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>bike_model_xg_boost</td>\n","      <td>{}</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                  NAME TAGS\n","0  bike_model_xg_boost   {}"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["#removing tag\n","\n","registry.remove_tag(model_name=model_name, model_version=model_version, tag_name=\"usage\")\n","registry.list_models().to_pandas()[[\"NAME\", \"TAGS\"]]"]},{"cell_type":"code","execution_count":29,"id":"700d23ee-33f4-4eee-8438-f1afa5cb52d1","metadata":{"id":"700d23ee-33f4-4eee-8438-f1afa5cb52d1","outputId":"4e8aace1-5a9f-4781-eca1-151d237a5f86"},"outputs":[],"source":["# #get tag value\n","# registry.set_tag(model_name=model_name, model_version=model_version, tag_name=\"usage\", tag_value=\"experiment\")\n","# print(registry.get_tag_value(model_name=model_name, model_version=model_version, tag_name=\"usage\"))\n","\n","# #get all tags\n","# print(registry.get_tags(model_name=model_name, model_version=model_version))"]},{"cell_type":"code","execution_count":null,"id":"66f8f151-40c9-407e-9f74-f1c9774280f9","metadata":{"id":"66f8f151-40c9-407e-9f74-f1c9774280f9","outputId":"25476513-74f5-48d8-b891-db9a10a72b4d"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.set_model_description() is in private preview since 0.2.0. Do not use it in production. \n","WARNING:snowflake.snowpark:ModelRegistry.get_model_description() is in private preview since 0.2.0. Do not use it in production. \n"]},{"name":"stdout","output_type":"stream","text":["this is a test model\n"]}],"source":["#MODEL DESCRIPTION\n","\n","registry.set_model_description(model_name=model_name, model_version=model_version, description=\"this is a test model\")\n","print(registry.get_model_description(model_name=model_name, model_version=model_version))"]},{"cell_type":"code","execution_count":null,"id":"eaec1cf5-9e7a-4245-9cfe-70d31e8554f9","metadata":{"id":"eaec1cf5-9e7a-4245-9cfe-70d31e8554f9","outputId":"4c21e673-c0ed-4aaf-8d0e-cbebb2a3300f"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.get_history() is in private preview since 0.2.0. Do not use it in production. \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>EVENT_TIMESTAMP</th>\n","      <th>EVENT_ID</th>\n","      <th>MODEL_ID</th>\n","      <th>ROLE</th>\n","      <th>OPERATION</th>\n","      <th>ATTRIBUTE_NAME</th>\n","      <th>VALUE[ATTRIBUTE_NAME]</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2024-04-16 06:26:18.541000-07:00</td>\n","      <td>e86d4bbbfbf411ee9fff34f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>REGISTRATION</td>\n","      <td>{\\n  \"CREATION_ENVIRONMENT_SPEC\": {\\n    \"pyth...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2024-04-16 06:26:20.124000-07:00</td>\n","      <td>e960f8a6fbf411ee8f6b34f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>METRICS</td>\n","      <td>{\\n  \"mean_abs_pct_err\": 164.38951873779297\\n}</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2024-04-16 06:30:39.508000-07:00</td>\n","      <td>83fc8b15fbf511ee919834f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>TAGS</td>\n","      <td>{\\n  \"usage\": \"experiment\"\\n}</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2024-04-16 06:31:06.149000-07:00</td>\n","      <td>93dd338ffbf511ee8af334f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>TAGS</td>\n","      <td>{\\n  \"usage\": \"experiment\"\\n}</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2024-04-16 06:31:07.648000-07:00</td>\n","      <td>94c24c92fbf511ee88f534f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>DESCRIPTION</td>\n","      <td>\"this is a test model\"</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                   EVENT_TIMESTAMP                          EVENT_ID  \\\n","0 2024-04-16 06:26:18.541000-07:00  e86d4bbbfbf411ee9fff34f39a51dc3f   \n","1 2024-04-16 06:26:20.124000-07:00  e960f8a6fbf411ee8f6b34f39a51dc3f   \n","2 2024-04-16 06:30:39.508000-07:00  83fc8b15fbf511ee919834f39a51dc3f   \n","3 2024-04-16 06:31:06.149000-07:00  93dd338ffbf511ee8af334f39a51dc3f   \n","4 2024-04-16 06:31:07.648000-07:00  94c24c92fbf511ee88f534f39a51dc3f   \n","\n","                           MODEL_ID            ROLE OPERATION ATTRIBUTE_NAME  \\\n","0  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET   REGISTRATION   \n","1  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET        METRICS   \n","2  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET           TAGS   \n","3  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET           TAGS   \n","4  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET    DESCRIPTION   \n","\n","                               VALUE[ATTRIBUTE_NAME]  \n","0  {\\n  \"CREATION_ENVIRONMENT_SPEC\": {\\n    \"pyth...  \n","1     {\\n  \"mean_abs_pct_err\": 164.38951873779297\\n}  \n","2                      {\\n  \"usage\": \"experiment\"\\n}  \n","3                      {\\n  \"usage\": \"experiment\"\\n}  \n","4                             \"this is a test model\"  "]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["\n","#REGISTRY HISTORY\n","registry.get_history().to_pandas()"]},{"cell_type":"code","execution_count":null,"id":"809869ca-70ae-44f7-b695-991e51f997b8","metadata":{"id":"809869ca-70ae-44f7-b695-991e51f997b8","outputId":"b2dfc376-bf8a-42cd-b62f-35cfc68ddbd6"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.get_model_history() is in private preview since 0.2.0. Do not use it in production. \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>EVENT_TIMESTAMP</th>\n","      <th>EVENT_ID</th>\n","      <th>MODEL_ID</th>\n","      <th>ROLE</th>\n","      <th>OPERATION</th>\n","      <th>ATTRIBUTE_NAME</th>\n","      <th>VALUE[ATTRIBUTE_NAME]</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2024-04-16 06:26:18.541000-07:00</td>\n","      <td>e86d4bbbfbf411ee9fff34f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>REGISTRATION</td>\n","      <td>{\\n  \"CREATION_ENVIRONMENT_SPEC\": {\\n    \"pyth...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2024-04-16 06:26:20.124000-07:00</td>\n","      <td>e960f8a6fbf411ee8f6b34f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>METRICS</td>\n","      <td>{\\n  \"mean_abs_pct_err\": 164.38951873779297\\n}</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2024-04-16 06:30:39.508000-07:00</td>\n","      <td>83fc8b15fbf511ee919834f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>TAGS</td>\n","      <td>{\\n  \"usage\": \"experiment\"\\n}</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2024-04-16 06:31:06.149000-07:00</td>\n","      <td>93dd338ffbf511ee8af334f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>TAGS</td>\n","      <td>{\\n  \"usage\": \"experiment\"\\n}</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2024-04-16 06:31:07.648000-07:00</td>\n","      <td>94c24c92fbf511ee88f534f39a51dc3f</td>\n","      <td>e00fa52bfbf411ee89ba34f39a51dc3f</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>SET</td>\n","      <td>DESCRIPTION</td>\n","      <td>\"this is a test model\"</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                   EVENT_TIMESTAMP                          EVENT_ID  \\\n","0 2024-04-16 06:26:18.541000-07:00  e86d4bbbfbf411ee9fff34f39a51dc3f   \n","1 2024-04-16 06:26:20.124000-07:00  e960f8a6fbf411ee8f6b34f39a51dc3f   \n","2 2024-04-16 06:30:39.508000-07:00  83fc8b15fbf511ee919834f39a51dc3f   \n","3 2024-04-16 06:31:06.149000-07:00  93dd338ffbf511ee8af334f39a51dc3f   \n","4 2024-04-16 06:31:07.648000-07:00  94c24c92fbf511ee88f534f39a51dc3f   \n","\n","                           MODEL_ID            ROLE OPERATION ATTRIBUTE_NAME  \\\n","0  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET   REGISTRATION   \n","1  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET        METRICS   \n","2  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET           TAGS   \n","3  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET           TAGS   \n","4  e00fa52bfbf411ee89ba34f39a51dc3f  \"ACCOUNTADMIN\"       SET    DESCRIPTION   \n","\n","                               VALUE[ATTRIBUTE_NAME]  \n","0  {\\n  \"CREATION_ENVIRONMENT_SPEC\": {\\n    \"pyth...  \n","1     {\\n  \"mean_abs_pct_err\": 164.38951873779297\\n}  \n","2                      {\\n  \"usage\": \"experiment\"\\n}  \n","3                      {\\n  \"usage\": \"experiment\"\\n}  \n","4                             \"this is a test model\"  "]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["\n","#MODEL HISTORY\n","registry.get_model_history(model_name=model_name, model_version=model_version).to_pandas()"]},{"cell_type":"code","execution_count":null,"id":"607e45b1-fc27-4dc4-a2ec-1de7b8b4a4f7","metadata":{"id":"607e45b1-fc27-4dc4-a2ec-1de7b8b4a4f7","outputId":"2bd2caa1-c3c3-470f-ead3-b246bc95ff6c"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.get_metric_value() is in private preview since 0.2.0. Do not use it in production. \n"]},{"data":{"text/plain":["164.38951873779297"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["#CHECK EVALUATION METRIC OF A MODEL\n","\n","registry.get_metric_value(model_name=model_name, model_version=model_version, metric_name=\"mean_abs_pct_err\")"]},{"cell_type":"code","execution_count":20,"id":"f17bad93-2cc5-407c-ac56-e8f4e7d85e67","metadata":{"id":"f17bad93-2cc5-407c-ac56-e8f4e7d85e67","outputId":"4ee42324-8d3d-4aed-f623-1a222c750dfe"},"outputs":[{"data":{"text/plain":["{'mean_abs_pct_err': 164.38951873779297}"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["#GET ALL METRICS STORED FOR A MODEL\n","registry.get_metrics(model_name=model_name, model_version=model_version)"]},{"cell_type":"code","execution_count":21,"id":"7e2e6d3e-f954-47b3-a7a0-35288943517d","metadata":{"id":"7e2e6d3e-f954-47b3-a7a0-35288943517d","outputId":"fc9532e7-bfff-4846-ffc3-6aaecf9b3e46"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.load_model() is in private preview since 0.2.0. Do not use it in production. \n"]},{"data":{"text/html":["<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n","             colsample_bylevel=None, colsample_bynode=None,\n","             colsample_bytree=None, early_stopping_rounds=None,\n","             enable_categorical=False, eval_metric=None,\n","             feature_types=[&#x27;int&#x27;, &#x27;int&#x27;, &#x27;int&#x27;, &#x27;float&#x27;, &#x27;float&#x27;, &#x27;float&#x27;,\n","                            &#x27;int&#x27;, &#x27;int&#x27;],\n","             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","             interaction_constraints=None, learning_rate=None, max_bin=None,\n","             max_cat_threshold=None, max_cat_to_onehot=None,\n","             max_delta_step=None, max_depth=3, max_leaves=None,\n","             min_child_weight=2, missing=nan, monotone_constraints=None,\n","             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n","             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n","             colsample_bylevel=None, colsample_bynode=None,\n","             colsample_bytree=None, early_stopping_rounds=None,\n","             enable_categorical=False, eval_metric=None,\n","             feature_types=[&#x27;int&#x27;, &#x27;int&#x27;, &#x27;int&#x27;, &#x27;float&#x27;, &#x27;float&#x27;, &#x27;float&#x27;,\n","                            &#x27;int&#x27;, &#x27;int&#x27;],\n","             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","             interaction_constraints=None, learning_rate=None, max_bin=None,\n","             max_cat_threshold=None, max_cat_to_onehot=None,\n","             max_delta_step=None, max_depth=3, max_leaves=None,\n","             min_child_weight=2, missing=nan, monotone_constraints=None,\n","             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n","             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"],"text/plain":["XGBRegressor(base_score=None, booster=None, callbacks=None,\n","             colsample_bylevel=None, colsample_bynode=None,\n","             colsample_bytree=None, early_stopping_rounds=None,\n","             enable_categorical=False, eval_metric=None,\n","             feature_types=['int', 'int', 'int', 'float', 'float', 'float',\n","                            'int', 'int'],\n","             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n","             interaction_constraints=None, learning_rate=None, max_bin=None,\n","             max_cat_threshold=None, max_cat_to_onehot=None,\n","             max_delta_step=None, max_depth=3, max_leaves=None,\n","             min_child_weight=2, missing=nan, monotone_constraints=None,\n","             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n","             predictor=None, random_state=None, ...)"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["#load model from registry\n","xg_boost_model = registry.load_model(model_name=model_name, model_version=model_version)\n","xg_boost_model"]},{"cell_type":"markdown","id":"8e7b4236","metadata":{},"source":["### Deploying Model From Registry \n","\n","* Code snippet delineates steps involved in deployed model registered in model registry\n","* Also explains steps to predict using deployed models"]},{"cell_type":"code","execution_count":22,"id":"423ded7c-94f0-4b11-a41d-6546d4309506","metadata":{"id":"423ded7c-94f0-4b11-a41d-6546d4309506","outputId":"49f7ea79-55cb-4a50-f8fd-7f40d5e6e5c3"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.deploy() is in private preview since 0.2.0. Do not use it in production. \n","WARNING:snowflake.snowpark.session:The version of package 'xgboost' in the local environment is 1.7.6, which does not fit the criteria for the requirement 'xgboost<2,>=1.7'. Your UDF might not work when the package version is different between the server and your local environment.\n"]},{"data":{"text/plain":["{'name': 'SNOWPARK_DEFINITVE_GUIDE.\"My_SCHEMA\".bike_model_xg_boost2_UDF',\n"," 'platform': <TargetPlatform.WAREHOUSE: 'warehouse'>,\n"," 'target_method': 'predict',\n"," 'signature': ModelSignature(\n","                     inputs=[\n","                         FeatureSpec(dtype=DataType.INT8, name='HOLIDAY'),\n"," \t\tFeatureSpec(dtype=DataType.INT8, name='WORKINGDAY'),\n"," \t\tFeatureSpec(dtype=DataType.INT8, name='HUMIDITY'),\n"," \t\tFeatureSpec(dtype=DataType.DOUBLE, name='TEMP'),\n"," \t\tFeatureSpec(dtype=DataType.DOUBLE, name='ATEMP'),\n"," \t\tFeatureSpec(dtype=DataType.DOUBLE, name='WINDSPEED'),\n"," \t\tFeatureSpec(dtype=DataType.INT8, name='SEASON'),\n"," \t\tFeatureSpec(dtype=DataType.INT8, name='WEATHER')\n","                     ],\n","                     outputs=[\n","                         FeatureSpec(dtype=DataType.FLOAT, name='output_feature_0')\n","                     ]\n","                 ),\n"," 'options': {'relax_version': True,\n","  'permanent_udf_stage_location': '@SNOWPARK_DEFINITVE_GUIDE.\"My_SCHEMA\"._SYSTEM_REGISTRY_DEPLOYMENTS_STAGE/bike_model_xg_boost2_UDF/'},\n"," 'details': {}}"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["#deploying a model from model registry\n","model_deployment_name = model_name + f\"{model_version}\" + \"_UDF\"\n","\n","registry.deploy(model_name=model_name,\n","                model_version=model_version,\n","                deployment_name=model_deployment_name,\n","                target_method=\"predict\",\n","                permanent=True,\n","                options={\"relax_version\": True})"]},{"cell_type":"code","execution_count":24,"id":"248f05d3-6d56-42ad-82c0-bb436ee6d035","metadata":{"id":"248f05d3-6d56-42ad-82c0-bb436ee6d035","outputId":"d29ddfd1-35c7-44ab-eb2d-1373721b3fb5"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.list_deployments() is in private preview since 1.0.1. Do not use it in production. \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>MODEL_NAME</th>\n","      <th>MODEL_VERSION</th>\n","      <th>DEPLOYMENT_NAME</th>\n","      <th>CREATION_TIME</th>\n","      <th>TARGET_METHOD</th>\n","      <th>TARGET_PLATFORM</th>\n","      <th>SIGNATURE</th>\n","      <th>OPTIONS</th>\n","      <th>STAGE_PATH</th>\n","      <th>ROLE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>bike_model_xg_boost</td>\n","      <td>2</td>\n","      <td>bike_model_xg_boost2_UDF</td>\n","      <td>2024-04-16 06:46:54.558000-07:00</td>\n","      <td>predict</td>\n","      <td>warehouse</td>\n","      <td>{\\n  \"inputs\": [\\n    {\\n      \"name\": \"HOLIDA...</td>\n","      <td>{\\n  \"permanent_udf_stage_location\": \"@SNOWPAR...</td>\n","      <td>@SNOWPARK_DEFINITVE_GUIDE.\"My_SCHEMA\"._SYSTEM_...</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            MODEL_NAME MODEL_VERSION           DEPLOYMENT_NAME  \\\n","0  bike_model_xg_boost             2  bike_model_xg_boost2_UDF   \n","\n","                     CREATION_TIME TARGET_METHOD TARGET_PLATFORM  \\\n","0 2024-04-16 06:46:54.558000-07:00       predict       warehouse   \n","\n","                                           SIGNATURE  \\\n","0  {\\n  \"inputs\": [\\n    {\\n      \"name\": \"HOLIDA...   \n","\n","                                             OPTIONS  \\\n","0  {\\n  \"permanent_udf_stage_location\": \"@SNOWPAR...   \n","\n","                                          STAGE_PATH            ROLE  \n","0  @SNOWPARK_DEFINITVE_GUIDE.\"My_SCHEMA\"._SYSTEM_...  \"ACCOUNTADMIN\"  "]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["#list all deployments for a model\n","registry.list_deployments(model_name, model_version).to_pandas()"]},{"cell_type":"code","execution_count":25,"id":"13b1f836-21f5-4ac7-8b09-d4be6ded7ed7","metadata":{"id":"13b1f836-21f5-4ac7-8b09-d4be6ded7ed7","outputId":"d3c12b9b-82a7-4ac6-f76e-d9e4065a5331"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelReference.predict() is in private preview since 0.2.0. Do not use it in production. \n","WARNING:snowflake.snowpark:ModelRegistry.get_deployment() is in private preview since 1.0.1. Do not use it in production. \n"]},{"name":"stdout","output_type":"stream","text":["----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n","|\"SEASON\"  |\"HOLIDAY\"  |\"WORKINGDAY\"  |\"WEATHER\"  |\"TEMP\"  |\"ATEMP\"  |\"HUMIDITY\"  |\"WINDSPEED\"       |\"CASUAL\"  |\"REGISTERED\"  |\"COUNT\"  |\"HOUR\"  |\"MONTH\"  |\"WEEKDAY\"  |\"output_feature_0\"  |\n","----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n","|1         |0          |0             |1          |9.84    |14.395   |75          |12.7993954069447  |0         |1             |1        |4       |1        |6          |31.198890686035156  |\n","|1         |0          |0             |1          |8.2     |12.88    |86          |12.7993954069447  |1         |2             |3        |7       |1        |6          |24.61685562133789   |\n","|1         |0          |0             |1          |15.58   |19.695   |76          |16.9979           |12        |24            |36       |10      |1        |6          |102.09496307373048  |\n","|1         |0          |0             |2          |18.86   |22.725   |72          |19.9995           |47        |47            |94       |13      |1        |6          |145.27708435058594  |\n","|1         |0          |0             |2          |18.86   |22.725   |72          |19.0012           |35        |71            |106      |14      |1        |6          |145.65347290039062  |\n","|1         |0          |0             |2          |17.22   |21.21    |82          |19.9995           |41        |52            |93       |16      |1        |6          |136.771728515625    |\n","|1         |0          |0             |3          |17.22   |21.21    |88          |16.9979           |6         |31            |37       |19      |1        |6          |74.54259490966797   |\n","|1         |0          |0             |2          |16.4    |20.455   |94          |15.0013           |11        |17            |28       |22      |1        |6          |86.20696258544922   |\n","|1         |0          |0             |2          |18.86   |22.725   |88          |19.9995           |15        |24            |39       |23      |1        |6          |110.6757583618164   |\n","|1         |0          |0             |2          |18.04   |21.97    |94          |16.9979           |1         |16            |17       |1       |1        |0          |84.95330047607422   |\n","----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n","\n"]}],"source":["model_ref = model_registry.ModelReference(registry=registry, model_name=model_name, model_version=model_version)\n","result_sdf = model_ref.predict(deployment_name=model_deployment_name, data=test_df)\n","result_sdf.show()"]},{"cell_type":"markdown","id":"40a49c41","metadata":{},"source":["### Delete Model From Deployment & Registry"]},{"cell_type":"code","execution_count":null,"id":"a162d5be-9110-46fc-983e-c99d8209593c","metadata":{"id":"a162d5be-9110-46fc-983e-c99d8209593c","outputId":"17f514ac-ff87-4ce2-82b3-6ae17d16990c"},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>MODEL_NAME</th>\n","      <th>MODEL_VERSION</th>\n","      <th>DEPLOYMENT_NAME</th>\n","      <th>CREATION_TIME</th>\n","      <th>TARGET_METHOD</th>\n","      <th>TARGET_PLATFORM</th>\n","      <th>SIGNATURE</th>\n","      <th>OPTIONS</th>\n","      <th>STAGE_PATH</th>\n","      <th>ROLE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Empty DataFrame\n","Columns: [MODEL_NAME, MODEL_VERSION, DEPLOYMENT_NAME, CREATION_TIME, TARGET_METHOD, TARGET_PLATFORM, SIGNATURE, OPTIONS, STAGE_PATH, ROLE]\n","Index: []"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":["#delete deployment\n","registry.delete_deployment(model_name=model_name,\n","                model_version=model_version,\n","                deployment_name=model_deployment_name)\n","registry.list_deployments(model_name, model_version).to_pandas()"]},{"cell_type":"code","execution_count":null,"id":"139f5b47-3ccc-437c-9d88-671037578cd3","metadata":{"id":"139f5b47-3ccc-437c-9d88-671037578cd3","outputId":"e3410f2e-87bb-4389-b4c0-9a2aa501f5dd"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:snowflake.snowpark:ModelRegistry.delete_model() is in private preview since 0.2.0. Do not use it in production. \n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CREATION_CONTEXT</th>\n","      <th>CREATION_ENVIRONMENT_SPEC</th>\n","      <th>CREATION_ROLE</th>\n","      <th>CREATION_TIME</th>\n","      <th>ID</th>\n","      <th>INPUT_SPEC</th>\n","      <th>NAME</th>\n","      <th>OUTPUT_SPEC</th>\n","      <th>RUNTIME_ENVIRONMENT_SPEC</th>\n","      <th>TYPE</th>\n","      <th>URI</th>\n","      <th>VERSION</th>\n","      <th>ARTIFACT_IDS</th>\n","      <th>DESCRIPTION</th>\n","      <th>METRICS</th>\n","      <th>TAGS</th>\n","      <th>REGISTRATION_TIMESTAMP</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>None</td>\n","      <td>{\\n  \"python\": \"3.10.12\"\\n}</td>\n","      <td>\"ACCOUNTADMIN\"</td>\n","      <td>2023-12-01 12:15:13.813000-08:00</td>\n","      <td>50c21c92908611ee966a82be22e42801</td>\n","      <td>None</td>\n","      <td>bike_model_gradient_boost</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>sklearn</td>\n","      <td>sfc://SNOWPARK.TUTORIAL.SNOWML_MODEL_50C21C929...</td>\n","      <td>1</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>{\\n  \"mean_abs_pct_err\": 68.89848373507952\\n}</td>\n","      <td>None</td>\n","      <td>2023-12-01 12:15:15.062000-08:00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  CREATION_CONTEXT    CREATION_ENVIRONMENT_SPEC   CREATION_ROLE  \\\n","0             None  {\\n  \"python\": \"3.10.12\"\\n}  \"ACCOUNTADMIN\"   \n","\n","                     CREATION_TIME                                ID  \\\n","0 2023-12-01 12:15:13.813000-08:00  50c21c92908611ee966a82be22e42801   \n","\n","  INPUT_SPEC                       NAME OUTPUT_SPEC RUNTIME_ENVIRONMENT_SPEC  \\\n","0       None  bike_model_gradient_boost        None                     None   \n","\n","      TYPE                                                URI VERSION  \\\n","0  sklearn  sfc://SNOWPARK.TUTORIAL.SNOWML_MODEL_50C21C929...       1   \n","\n","  ARTIFACT_IDS DESCRIPTION                                        METRICS  \\\n","0         None        None  {\\n  \"mean_abs_pct_err\": 68.89848373507952\\n}   \n","\n","   TAGS           REGISTRATION_TIMESTAMP  \n","0  None 2023-12-01 12:15:15.062000-08:00  "]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["#delete model from registry\n","registry.delete_model(model_name=model_name,\n","                model_version=model_version)\n","registry.list_models().to_pandas()"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.16"}},"nbformat":4,"nbformat_minor":5}
